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We introduce a tracking-by-detection method that integrates a deep object detector with a particle filter tracker under the regularization framework where the tracked object is represented by a sparse dictionary. A novel observation model which establishes consensus between the detector and tracker is formulated that enables us to update the dictionary with the guidance of the deep detector. This yields an efficient representation of the object appearance through the video sequence hence improves robustness to occlusion and pose changes. The proposed tracker employs a 7D affine state vector formulated to output deformed object bounding boxes that significantly increases robustness to scale changes. Performance evaluation has been carried out on a subset of challenging VOT2016 and VOT2018 benchmarking video sequences for 80 object classes of COCO. Numerical results demonstrate that the introduced tracker, L1DPF-M, achieves comparable robustness while it outperforms state-of-the-art trackers in success rate where the improvement achieved at IoU-th = 0.5 on the used VOT2016 and VOT2018 sequences is 11% and 9%, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we propose a proximity-based indoor positioning system which is capable of monitoring mobile device user's indoor locations where the commonly used GPS signal is unavailable or weak. The designed system is aimed to be integrated into a context-aware communications system to prevent transmission of irrelevant content to all users but easing delivery of the location-based information. Similar to the beacon technology that assigns a code to each targeted position in an indoor location, our system labels the locations with audio watermark codes where user's mobile device monitors and receives the watermarked audio. The proposed encoder performs code-division multiplexing that allows insertion of several location indexes into the same audio file. Watermark embedded through spread spectrum improves robustness to noise and guarantees a satisfactory performance even though the mobile device has a low band microphone. The designed decoder installs synchronization between the mobile device and the watermarked audio emitter in real time, and extracts the embedded watermark code words assigned to specific indoor locations. This invokes the context-aware content delivery module and the delivery is initiated. Position displacements of the mobile users are estimated by the time-of-flight technique and the users moving within the coverage range of the emitters are continuously monitored. Decoding is achieved in real time that enables the mobile users to reach to content delivered from different emitters within their coverage range. Performance tests demonstrate that the developed system enables to estimate the user position within the 7-m distance from the emitter while keeping inaudibility. We reached 2-m spatial resolution in discrimination of different emitters. The proposed framework can be considered as a promising alternative to latest technologies, i.e., Wi-Fi-based fingerprinting systems or beacons. [ABSTRACT FROM AUTHOR]

We propose a method based on perceptual prosodic features for medium term speaker state classification, particularly sleepiness detection. Unlike existing methods, our features represent spectral characteristics of speech in perceptual bands and also track temporal content omitting any linguistic segmentation. Despite conventional methods, we aim to model transitions between non-sleepy and sleepy modes rather than emotional states. Along with the proposed compact feature set, the developed system enable discrimination of medium term speaker states with a lower complexity compared to existing systems. This is achieved by constructing a dictionary for speech data based on bag-of-words concept. It has been identified that a training setup which is based on learned codewords, yields a robust classifier for sleepy speech. The speaker state classification has been performed by applying a two-class classification scheme on the observed test data. The numerical results, obtained on the Sleepy Language Corpus (SLC) by using Support Vector Machines (SVM) classifier, demonstrate a 10% improvement on average on unweighted recall rates compared to the benchmarking results. The introduced method is promising for online applications because of its frame based feature extraction scheme which differs from conventional segmental descriptor extraction techniques. [ABSTRACT FROM AUTHOR]

Summary As the retransmission mechanism of the Session Initiation Protocol (SIP) significantly increases the amount of incoming traffic and makes an overloaded SIP server unstable, the models created for a stable SIP system cannot be effectively used to evaluate the performance of the overloaded server. To overcome this difficulty, in this paper, we present a fluid-flow model to analyze the priority-based request scheduling mechanism (PRSM) behavior in the overloaded SIP server equipped with an infinite buffer. The PRSM has been proposed in our earlier studies to prevent the overloading of SIP servers, where the original SIP request messages have strict priority over the retransmissions. The performance evaluation of the proposed model for the PRSM and the reference model of the conventional SIP has been reported under various test scenarios. The experiment results of the proposed model verify the effectiveness of the PRSM fluid-flow model as we already demonstrated in our previous studies using our simulation tool. Without loss of generality, the numerical results closely match with the simulation results, indicating that the proposed models can accurately capture the dynamic behavior of a SIP server with the PRSM. [ABSTRACT FROM AUTHOR]

With the widespread deployments of voice-over-internet protocol services, the existing session initiation protocol (SIP) design cannot scale up for large network sizes. Events triggering a demand burst or a server slowdown can cause SIP server overload, overload propagation, and crash, thus bringing down the whole SIP network. Since the SIP retransmission mechanism exacerbates the overload condition, existing models created for a stable SIP system cannot be effectively used to analyze an overloaded server. In this paper, we propose a fluid-flow model to characterize the behavior of the finite buffer SIP server equipped with priority-based request scheduling mechanism (PRSM). The model for the PRSM uses primary and secondary queues for the original request messages and the retransmitted requests, respectively. The performance metrics, namely, the failed call attempts and the response delay from sending INVITE request until receiving a 100-Trying response, are derived using the arrival time-slot tracking and the removal processes of the proposed fluid-flow model. We conducted test cases under the heavy traffic conditions, where the overload is caused by bulk and bursty arrivals or server slowdown. The numerical results closely match with the simulation results for all experiments, indicating that the proposed model can accurately capture the dynamic behavior of an SIP server with the PRSM. The experiments demonstrate that the number of failed call attempts is close to 0 and the mean response delay is kept constant around 175 ms for the PRSM when the buffer size is higher than 1K while both metrics are significantly higher for the conventional SIP. [ABSTRACT FROM AUTHOR]